diff --git a/src/transformers/models/bloom/modeling_bloom.py b/src/transformers/models/bloom/modeling_bloom.py
index 02f682d01118..8fabde20b927 100644
--- a/src/transformers/models/bloom/modeling_bloom.py
+++ b/src/transformers/models/bloom/modeling_bloom.py
@@ -298,7 +298,7 @@ def forward(
         else:
             present = None
 
-        # # [batch_size*num_heads, head_dim, q_length] x [batch_size*num_heads, head_dim, k_length] -> [batch_size*num_heads, q_length, k_length]
+        # # [batch_size*num_heads, head_dim, q_length] x [batch_size*num_heads, head_dim, kv_length] -> [batch_size*num_heads, q_length, kv_length]
         matmul_result = torch.baddbmm(
             input=alibi,
             batch1=query_layer,
@@ -307,10 +307,10 @@ def forward(
             alpha=self.inv_norm_factor,
         )
 
-        # change view to [batch_size, num_heads, q_length, k_length]
+        # change view to [batch_size, num_heads, q_length, kv_length]
         attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
 
-        # we cast attention scores to fp32, compute scaled softmax and cast back into initial dtype - [batch_size, num_heads, q_length, k_length]        input_dtype = attention_scores.dtype
+        # we cast attention scores to fp32, compute scaled softmax and cast back into initial dtype - [batch_size, num_heads, q_length, kv_length]        input_dtype = attention_scores.dtype
         input_dtype = attention_scores.dtype
         attention_scores = attention_scores.float()
         attn_weights = torch.masked_fill(
@@ -318,13 +318,13 @@ def forward(
         )
         attention_probs = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(input_dtype)
 
-        # [batch_size, num_heads, q_length, k_length]
+        # [batch_size, num_heads, q_length, kv_length]
         attention_probs = self.attention_dropout(attention_probs)
 
         if head_mask is not None:
             attention_probs = attention_probs * head_mask
 
-        # change view [batch_size x num_heads, q_length, k_length]
+        # change view [batch_size x num_heads, q_length, kv_length]
         attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length)
 
         # matmul: [batch_size * num_heads, q_length, head_dim]
@@ -890,8 +890,14 @@ def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) ->
         # value: layer_past[1] [batch_size * num_heads, seq_length, head_dim]
         return tuple(
             (
-                layer_past[0].view(batch_size, num_heads, head_dim, seq_length).index_select(0, beam_idx.to(layer_past[0].device)).view(batch_size_times_num_heads, head_dim, seq_length),
-                layer_past[1].view(batch_size, num_heads, seq_length, head_dim).index_select(0, beam_idx.to(layer_past[1].device)).view(batch_size_times_num_heads, seq_length, head_dim)
+                layer_past[0]
+                .view(batch_size, num_heads, head_dim, seq_length)
+                .index_select(0, beam_idx.to(layer_past[0].device))
+                .view(batch_size_times_num_heads, head_dim, seq_length),
+                layer_past[1]
+                .view(batch_size, num_heads, seq_length, head_dim)
+                .index_select(0, beam_idx.to(layer_past[1].device))
+                .view(batch_size_times_num_heads, seq_length, head_dim),
             )
             for layer_past in past
         )